Machine Learning Engineer (AI)

Machine Learning Engineer - Document Intelligence & Applied GenAI

Company

PandaDoc

Conditions

Full-time Middle 🌎 World 💻 Development 🏠 Remote Job description

What you'll do:

• Model Development & Evaluation
  • Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction.
  • Define metrics, benchmarks, and validation strategies for real-world document workloads.
• Dataset & Pipeline Creation
  • Design and curate high-quality datasets for supervised training, fine-tuning, and validation.
  • Create scalable preprocessing pipelines for PDFs, scans, images, forms, and semi-structured documents.
• Model Training & Fine-Tuning
  • Train and fine-tune transformer-based OCR, VLMs, layout models, and open-source LLMs for document understanding tasks.
  • Optimize models for reliability, accuracy, and cost efficiency in production environments.
• Inference & Deployment
  • Deploy ML models with modern inference runtimes (vLLM, TGI, TensorRT, ONNX Runtime).
  • Build guardrails, monitoring, and fallback mechanisms to ensure safe and predictable model behavior.
• RAG & Document Reasoning
  • Develop retrieval and chunking strategies tailored to document structures (tables, forms, multi-page PDFs).
  • Optimize end-to-end RAG pipelines for semantic search, Q&A, and workflow automation.
• Cross-Functional Collaboration
  • Partner with PMs, backend engineers, and product designers to define AI opportunities and translate requirements into technical solutions.

Who you are:

We are expanding our AI/ML function with an ML Engineer who specializes in document intelligence , vision–language models , and LLM-based extraction and reasoning. You should be comfortable with both traditional document AI approaches and cutting-edge GenAI workflows. You thrive in fast-moving environments, are self-directed, and enjoy solving practical ML problems that directly impact customers. We’re looking for someone with experience in:

• Vision transformers, layout models, and OCR systems
• Structured extraction from complex documents
• RAG for document-heavy workloads
• Optimizing LLM pipelines for cost, accuracy, and throughput
• Deploying and benchmarking models in real production systems

Required Experience:

• 5+ years of Python experience
• Experience training, fine-tuning, and deploying traditional computer vision models for document intelligence tasks (layout detection, table extraction, OCR, information extraction)
• Hands-on experience with document understanding frameworks and models:
  • Traditional document AI models (LayoutLM, Donut, DocFormer)
  • Modern vision-language models with OCR capabilities (DeepSeek-OCR, LightOnOCR-1B, etc.)
  • Experience deploying and optimizing models using inference frameworks such as vLLM (preferred), TGI, TensorRT, or ONNX Runtime
  • Experience applying LLMs to document intelligence workflows, including both frontier models and open-source alternatives
  • Strong understanding of coordinate systems and spatial reasoning for absolute positioning and field detection in forms/documents

It would be awesome if you had:

• Familiarity with PDF parsing libraries and document preprocessing pipelines
• Experience fine-tuning open-source models for domain-specific document tasks
• Knowledge of evaluation metrics for document understanding tasks (F1, exact match, etc.)

Benefits:

• An honest, open culture that emphasizes feedback and promotes professional and personal development
• An opportunity to work from anywhere — our team is distributed worldwide, from Lisbon to Manila, from Florida to California
• 6 self care days
• A competitive salary
• And much more!

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